# Copyright (c) Alibaba, Inc. and its affiliates.
# demo_seq_cls: https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_vl/infer.py
import os
from typing import List

os.environ["CUDA_VISIBLE_DEVICES"] = "0"


def infer_batch(engine: "InferEngine", infer_requests: List["InferRequest"]):
    resp_list = engine.infer(infer_requests)
    query0 = infer_requests[0].messages[0]["content"]
    query1 = infer_requests[1].messages[0]["content"]
    print(f"query0: {query0}")
    print(f"response0: {resp_list[0].choices[0].message.content}")
    print(f"query1: {query1}")
    print(f"response1: {resp_list[1].choices[0].message.content}")


if __name__ == "__main__":
    # This is an example of BERT with LoRA.
    from swift.llm import (
        InferEngine,
        InferRequest,
        PtEngine,
        load_dataset,
        safe_snapshot_download,
        BaseArguments,
    )
    from swift.tuners import Swift

    adapter_path = safe_snapshot_download("swift/test_bert")
    args = BaseArguments.from_pretrained(adapter_path)
    args.max_length = 512
    args.truncation_strategy = "right"
    # method1
    model, processor = args.get_model_processor()
    model = Swift.from_pretrained(model, adapter_path)
    template = args.get_template(processor)
    engine = PtEngine.from_model_template(model, template, max_batch_size=64)

    # method2
    # engine = PtEngine(args.model, adapters=[adapter_path], max_batch_size=64,
    #                   task_type=args.task_type, num_labels=args.num_labels)
    # template = args.get_template(engine.processor)
    # engine.default_template = template

    # Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
    dataset = load_dataset(["DAMO_NLP/jd:cls#1000"], seed=42)[0]
    print(f"dataset: {dataset}")
    infer_requests = [InferRequest(messages=data["messages"]) for data in dataset]
    infer_batch(engine, infer_requests)

    infer_batch(
        engine,
        [
            InferRequest(messages=[{"role": "user", "content": "今天天气真好呀"}]),
            InferRequest(messages=[{"role": "user", "content": "真倒霉"}]),
        ],
    )
